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Intelligent Workloads at the Edge

Intelligent Workloads at the Edge

By : Indraneel (Neel) Mitra, Ryan Burke
4.8 (17)
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Intelligent Workloads at the Edge

Intelligent Workloads at the Edge

4.8 (17)
By: Indraneel (Neel) Mitra, Ryan Burke

Overview of this book

The Internet of Things (IoT) has transformed how people think about and interact with the world. The ubiquitous deployment of sensors around us makes it possible to study the world at any level of accuracy and enable data-driven decision-making anywhere. Data analytics and machine learning (ML) powered by elastic cloud computing have accelerated our ability to understand and analyze the huge amount of data generated by IoT. Now, edge computing has brought information technologies closer to the data source to lower latency and reduce costs. This book will teach you how to combine the technologies of edge computing, data analytics, and ML to deliver next-generation cyber-physical outcomes. You’ll begin by discovering how to create software applications that run on edge devices with AWS IoT Greengrass. As you advance, you’ll learn how to process and stream IoT data from the edge to the cloud and use it to train ML models using Amazon SageMaker. The book also shows you how to train these models and run them at the edge for optimized performance, cost savings, and data compliance. By the end of this IoT book, you’ll be able to scope your own IoT workloads, bring the power of ML to the edge, and operate those workloads in a production setting.
Table of Contents (17 chapters)
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1
Section 1: Introduction and Prerequisites
3
Section 2: Building Blocks
10
Section 3: Scaling It Up
13
Section 4: Bring It All Together

Synchronizing the state between the edge and the cloud

The HBS hub device, if made into a real product, would connect with local devices over a network protocol and proxy telemetry and commands with a cloud service. In the previous chapter, we used components running on our hub device to interface with local hardware interfaces on the Raspberry Pi Sense HAT.

This makes sense when the hub device communicates with hardware over serial interfaces, but when communicating over a network, those appliance monitoring kits won't really be software components running on the hub device using the Greengrass IPC interface to exchange messages. Instead, they may use a network protocol such as Message Queue Telemetry Transport (MQTT) to exchange messages with the hub device over Wi-Fi or Bluetooth.

In this section, you will deploy new managed components for connecting to leaf devices over MQTT and synchronize the state of a leaf device's telemetry to the cloud.

Introduction to...

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